Managing files according to categories

ABSTRACT

According to various embodiments, with respect to a target set of files being managed (e.g., protected by data snapshots), each file in the target set of files is classified into one of two or more filesets (discontiguous filesets), where each of these filesets comprises one or more files that are related to each other by one or more factors, such as frequency of file change or purpose of existence (e.g., used by a software application). Once classified, files within the target set of files can be uniquely processed by a data management operation (e.g., incremental data snapshot process) according to their association to a discontiguous fileset.

TECHNICAL FIELD

Embodiments described herein relate to managing data and, moreparticularly, to systems, methods, devices, and machine-readable mediafor managing files (e.g., as filesets) based on categories.

BACKGROUND

Conventional file management (e.g., for backup and recovery) usuallyinvolves grouping files according to hierarchical relationships and,more specifically, according to location of files (e.g., in a directoryhierarchy provided by a filesystem). For instance, on a client computingdevice, a user can configure a data backup and recovery service toprotect a set of files (also referred to as a fileset) that comprises anentire root file system (e.g., all files starting from the rootdirectory (“/”) of the client file system). This service can be furtherconfigured by the user to protect the user-specified fileset accordingto a backup policy (e.g., as defined by a service level agreement (SLA)between the user and a third-party data backup service provider), whichcan define how frequently a data snapshot (e.g., full or incremental) ofthe user-specified fileset will be generated for data protection.

At a high level, a traditional process for generating an incrementaldata snapshot involves: (a) a scan phase where metadata (e.g., by astat( ) system call) for each file that is targeted for data backupprotection is obtained (e.g., fetched) to determine which files haveexperienced a data block change or a file metadata change since the lastdata snapshot; and (b) a backup phase where data from those files (inthe fileset) that have experienced a data block change or a filemetadata change is obtained and stored (e.g., as delta data) as part ofthe incremental data snapshot. Generally, the time taken to perform thescan phase is usually directly proportional to the number of files. Thiscan make it undesirable to perform a traditional process for generatingan incremental data snapshot on certain filesets, such as a filesetdefined as the root directory of a filesystem (e.g., of a virtualmachine or a client computing device), where the fileset covers a lot offiles and a large number of those files remain unchanged betweenconsecutive data snapshots.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate variousembodiments of the present disclosure and should not be considered aslimiting its scope. To easily identify the discussion of any particularelement or act, the most significant digit or digits in a referencenumber refer to the figure (“FIG.”) number in which that element or actis first introduced.

FIG. 1A is a block diagram illustrating an example networked computingenvironment in which some embodiments described herein are practiced.

FIG. 1B is a block diagram illustrating an example of a server,according to some embodiments of the present disclosure.

FIG. 1C is a block diagram illustrating an example of a storageappliance, according to some embodiments of the present disclosure.

FIGS. 2 through 7 are flowcharts illustrating example methods ofmanaging files according to categories, in accordance with someembodiments.

FIG. 8 is a block diagram illustrating an example architecture ofsoftware that can be used to implement various embodiments describedherein.

FIG. 9 illustrates a diagrammatic representation of an example machinein the form of a computer system within which a set of instructions maybe executed for causing the machine to perform any one or more of themethodologies of various embodiments described herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

As discussed herein, conventional file management can involve groupingfiles according to hierarchical relation (e.g., based on directoryhierarchy), and the time taken to perform a traditional process forgenerating an incremental data snapshot of a fileset can be wastefulwhen the fileset covers a lot of files and a large number of those filesremain unchanged between consecutive data snapshots (e.g., full orincremental data snapshots). For example, a fileset covering a typicaloperating system's root filesystem can comprise a very large number offiles, and this large number of files can take time for a scan phase ofa traditional incremental data snapshot process to process. This can bea waste of time given that portions of such a fileset remain unchangedbetween consecutive data snapshots.

Various embodiments described herein address the deficiencies oftraditional classification of files in filesets and performingtraditional processes for generating incremental data snapshots offilesets. According to various embodiments, with respect to a target setof files being managed (e.g., protected by data snapshots), each file inthe target set of files is classified into one of two or more filesets(also referred herein to as discontiguous filesets), where each of thesefilesets comprises one or more files that are related to each other byone or more factors, such as frequency of file change or purpose ofexistence (e.g., used by a software application). Once classified, fileswithin the target set of files can be uniquely processed by a datamanagement operation (e.g., incremental data snapshot process) accordingto their association to a discontiguous fileset (e.g., rather than beingprocessed according to their location within a directory hierarchy).

The factors used to determine relation of two or more files can include,without limitation, the frequency of data changes (e.g., data blockchanges or file metadata changes) experienced by the two or more filesover time (e.g., over a sample space of x number of prior datasnapshots), or common purpose of existence of the two or more files. Forinstance, a certain set of files can be co-related if one or moresoftware applications change data content in the certain set of filesmore frequently than other files. As another instance, with respect toan operating system environment, files can be installed using packagesand those package files can represent a set of files that are related toeach other by a common purpose (e.g., associated with use by a softwareapplication installed by a package). For some embodiments, files of acertain discontiguous fileset can be related by one or more factors thatdo not include hierarchical relation (e.g., the one or more factors canexclude location of files within a directory hierarchy).

For some embodiments, to classify each file in a target set of filesinto a discontiguous fileset, each file in the target set of files isranked based on one or more factors, where files having a rank thatfalls into a similar range of ranks can be considered related and can begrouped into a common discontiguous fileset. The rank of a file can bedetermined, for example, by how often the data content of the given filechanged over time (e.g., over a sample space of x number of prior datasnapshots), by the significance of the given file to a softwareapplication that is using (e.g., consuming) the given file, or by somecombination thereof. For instance, a given software application thatuses (e.g., consumes) files from a target set of files can be associatedwith a significance rank, which can be considered in the ranking of anyfile (from the target set of files) that is used by the given softwareapplication. The significance rank of a software application can, forexample, range from 1 to n. For this example range, 1 can representeither most or least significant rank in the range, and s can bedetermined by the number of software applications being considered withrespect to the target set of applications. For instance, s can be equalto m+1, where in number of known software applications are beingconsidered. The significance rank of s can represent the lowestsignificance rank, and can be the default rank for software applicationsthat are unknown or not considered important.

For some embodiments, a plurality of fileset classes that representdifferent discontiguous filesets are used to categorize (by filesetclass association) files from a target set of files into the differentdiscontiguous filesets. For example, an embodiment can use (e.g.,define) three fileset classes (e.g., fileset classes A, B, and C) forclassifying files from a target set of files into three differentdiscontiguous filesets. For instance, based on the rank of a given file,the given file can be associated with fileset class A if it has a rankranging from 50 to 100, associated with fileset class B if it has a rankranging from 10 to less than 50, and associated with fileset class C ifit has a rank ranging from 0 to less than 10. For purposes of datamanagement (e.g., data protection via data snapshots), a file associatedwith fileset class A can be considered more important for datamanagement (e.g., data protection) than any file associated with filesetclass B, and a file associated with fileset class B can be consideredmore important for data management than any file associated with filesetclass C. At the beginning of data managing (e.g., data protecting) atarget set of files, all files in the target set of files can beassociated with a default fileset class (e.g., one of fileset classes A,B, or C). Eventually, over time (e.g., generation of incremental datasnapshots), the rank of each of the files (in the target set of files)can be periodically determined (e.g., updated prior to generation of anew incremental data snapshot) and the files can be re-classified (e.g.,demoted or promoted) from the default fileset class to another filesetclass based on the newly determined (e.g., updated) rank. For someembodiments, each file is initially associated with class A, and caneventually be associated with another class (e.g., B or C) by a processthat periodically ranks and classifies (e.g., re-ranks andre-classifies) files to fileset classes. Depending on the embodiment,the process of periodically ranking and classifying files to filesetclasses can begin after minimum amount of time has passed. For instance,the process of periodically ranking and classifying files to filesetclasses can begin after a minimal number of incremental data snapshotshave been generated, thereby providing a sufficient sample size uponwhich to determine the ranking of files (e.g., based on the factor offrequency of data content change).

According to various embodiments, a data management operation isperformed differently, or at a different frequency, with respect to eachdiscontiguous fileset. For example, for some embodiments, a datasnapshot is generated at a different frequency for each of thediscontiguous filesets. For example, with respect to the threediscontiguous filesets represented by fileset classes A, B, and C, anembodiment can generate an incremental data snapshot for (or performsome other data management operation on) files in the discontiguousfileset of the fileset classes A every 1 hour, for files in thediscontiguous fileset of the fileset classes B every 12 hours, and filesin the discontiguous fileset of the fileset classes C at a frequency ofevery 24 hours. Depending on the embodiments, the frequency of at leastone of the fileset classes (e.g., A, B, or C) can be defined accordingto a service level agreement (SLA) between a service provider (e.g.,organization providing the data snapshot service) and a client (e.g.,user or organization that owns the target set of files). For someembodiments, a data snapshot frequency of a single fileset class isdefined (e.g., by a pre-defined or custom defined SLA that provides afrequency guarantee) as a base frequency, and the remaining filesetclasses are automatically defined relative to that single fileset class.For instance, where fileset class B serves as a base frequency forgenerating data snapshot incremental data snapshot), the data snapshotgeneration frequency of fileset class A can be defined to be a constantx more frequent than the base frequency of fileset class B, while thedata snapshot generation frequency of fileset class C can be defined tobe a constant y less frequent than the base frequency of fileset classB. For some embodiments, data protection for all files initially startswith a base data snapshot frequency defined by an SLA that is standardor custom defined and, eventually, each of the discontiguous filesetshas a different data snapshot frequency.

As used herein, a fileset can comprise a set of files within a filesystem. For some embodiments, the set of files associated with a givenfileset is not necessarily defined by location of files within adirectory hierarchy. As used herein, a discontiguous fileset comprises aset of files that are related to each other based on the frequency bywhich the files experience data block changes or file metadata changes(e.g., data updates) based on the common purpose of those files. Forinstance, a first fileset can comprise one or more files associated witha user software application of high importance to a user, while a secondfileset can comprise one or more files that experience data updatesevery couple of minutes (e.g., every 5 minutes).

As used herein, a data snapshot (or also referred to as just a“snapshot”) can comprise a storage snapshot of a set of files stored ona filesystem, such as one associated with a virtual machine (e.g., allthe files from the root directory of the virtual machine). A datasnapshot can capture a state of the set of files at a particular pointin time. A data snapshot can either be a full data snapshot of the setof files, or an incremental data snapshot of the set of files. A fulldata snapshot can comprise a full data image (e.g., full copies) of aset of files. A full data snapshot can serve as an anchor snapshot for asnapshot chain that comprises one or more incremental data snapshots. Anincremental data snapshot can be generated based on (e.g., relative to)a previously generated full data snapshot that serves as its base (e.g.,anchor snapshot).

Though various embodiments are described herein with respect to usingranking and classification of files into discontiguous filesets for datasnapshot purposes, for some embodiments, the ranking and classificationof files described herein can be used with other data managementoperations, such as how the files are stored or determining whichcomputing devices (e.g., in a data center) are used to store or processthe files.

Reference will now be made in detail to embodiments of the presentdisclosure, examples of which are illustrated in the appended drawings.The present disclosure may, however, be embodied in many different formsand should not be construed as being limited to the embodiments setforth herein.

FIG. 1A is a block diagram illustrating an example networked computingenvironment 100 in which some embodiments described herein arepracticed. As depicted, the networked computing environment 100 includesa data center 150, a storage appliance 140, and a computing device 154in communication with each other via one or more networks 180. Thenetworked computing environment 100 may include a plurality of computingdevices interconnected through one or more networks 180. The one or morenetworks 180 may allow computing devices and/or storage devices toconnect to and communicate with other computing devices and/or otherstorage devices. In some cases, the networked computing environment mayinclude other computing devices and/or other storage devices not shown.The other computing devices can include, for example, a mobile computingdevice, a non-mobile computing device, a server, a workstation, a laptopcomputer, a tablet computer, a desktop computer, or an informationprocessing system. The other storage devices can include, for example, astorage area network storage device, a networked-attached storagedevice, a hard disk drive, a solid-state drive, or a data storagesystem.

The data center 150 can include one or more servers, such as server 160,in communication with one or more storage devices, such as storagedevice 156. The one or more servers can also be in communication withone or more storage appliances, such as storage appliance 170. Theserver 160, storage device 156, and storage appliance 170 can be incommunication with each other via a networking fabric connecting serversand data storage units within the data center 150 to each other. Thestorage appliance 170 can include a data management system for backingup virtual machines and/or files within a virtualized infrastructure.The server 160 can be used to create and manage one or more virtualmachines associated with a virtualized infrastructure.

The one or more virtual machines can run various applications, such as acloud-based service, a database application or a web server (e.g., a webserver hosting an auto-parts website). The storage device 156 caninclude one or more hardware storage devices for storing data, such as ahard disk drive (HDD), a magnetic tape drive, a solid-state drive (SSD),a storage area network (SAN) storage device, or a networked-attachedstorage (NAS) device. In some cases, a data center, such as data center150, can include thousands of servers and/or data storage devices incommunication with each other. The data storage devices can comprise atiered data storage infrastructure (or a portion of a tiered datastorage infrastructure). The tiered data storage infrastructure canallow for the movement of data across different tiers of a data storageinfrastructure between higher-cost, higher-performance storage devices(e.g., solid-state drives and hard disk drives) and relativelylower-cost, lower-performance storage devices (e.g., magnetic tapedrives).

The one or more networks 180 can include a secure network such as anenterprise private network, an unsecure network such as a wireless opennetwork, a local area network (LAN), a wide area network (WAN), and theInternet. The one or more networks 180 can include a cellular network, amobile network, a wireless network, or a wired network. Each network ofthe one or more networks 180 can include hubs, bridges, routers,switches, and wired transmission media such as a direct-wiredconnection. The one or more networks 180 can include an extranet orother private network for securely sharing information or providingcontrolled access to applications or files.

A server, such as server 160, can allow a client to download informationor files (e.g., executable, text, application, audio, image, or videofiles) from the server 160 or to perform a search query related toparticular information stored on the server 160. In some cases, a servercan act as an application server or a file server. In general, a servercan refer to a hardware device that acts as the host in a client-serverrelationship or a software process that shares a resource with orperforms work for one or more clients.

One embodiment of server 160 includes a network interface 165, processor166, memory 167, disk 168, and virtualization manager 169 all incommunication with each other. Network interface 165 allows server 160to connect to one or more networks 180. Network interface 165 caninclude a wireless network interface and/or a wired network interface.Processor 166 allows server 160 to execute computer readableinstructions stored in memory 167 in order to perform processesdescribed herein. Processor 166 can include one or more processingunits, such as one or more CPUs and/or one or more GPUs. Memory 167 cancomprise one or more types of memory (e.g., RAM, SRAM, DRAM, ROM,EEPROM, Flash, etc.). Disk 168 can include a hard disk drive and/or asolid-state drive. Memory 167 and disk 168 can comprise hardware storagedevices.

The virtualization manager 169 can manage a virtualized infrastructureand perform management operations associated with the virtualizedinfrastructure. The virtualization manager 169 can manage theprovisioning of virtual machines running within the virtualizedinfrastructure and provide an interface to computing devices interactingwith the virtualized infrastructure. In one example, the virtualizationmanager 169 can set a virtual machine into a frozen state in response toa snapshot request made via an application programming interface (API)by a storage appliance, such as storage appliance 140 or storageappliance 170. Setting the virtual machine into a frozen state can allowa point in time snapshot of the virtual machine to be stored ortransferred. In one example, updates made to a virtual machine that hasbeen set into a frozen state can be written to a separate file (e.g., anupdate file) while the virtual disk file associated with the state ofthe virtual machine at the point in time is frozen. The virtual diskfile can be set into a read-only state to prevent modifications to thevirtual disk file while the virtual machine is in the frozen state. Thevirtualization manager 169 can then transfer data associated with thevirtual machine (e.g., an image of the virtual machine or a portion ofthe image of the virtual machine) to a storage appliance in response toa request made by the storage appliance. After the data associated withthe point in time snapshot of the virtual machine has been transferredto the storage appliance, the virtual machine can be released from thefrozen state (i.e., unfrozen) and the updates made to the virtualmachine and stored in the separate file can be merged into the virtualdisk file. The virtualization manager 169 can perform various virtualmachine related tasks, such as cloning virtual machines, creating newvirtual machines, monitoring the state of virtual machines, movingvirtual machines between physical hosts for load balancing purposes, andfacilitating backups of virtual machines.

One embodiment of storage appliance 170 includes a network interface175, processor 176, memory 177, and disk 178 all in communication witheach other. Network interface 175 allows storage appliance 170 toconnect to one or more networks 180. Network interface 175 may include awireless network interface and/or a wired network interface. Processor176 allows storage appliance 170 to execute computer-readableinstructions stored in memory 177 in order to perform processesdescribed herein. Processor 176 can include one or more processingunits, such as one or more CPUs and/or one or more GPUs. Memory 177 cancomprise one or more types of memory (e.g., RAM, SRAM, DRAM, ROM,EEPROM, NOR Flash, NAND Flash, etc.). Disk 178 can include a hard diskdrive and/or a solid-state drive. Memory 177 and disk 178 can comprisehardware storage devices.

In one embodiment, the storage appliance 170 can include four machines.Each of the four machines can include a multi-core CPU, 64 GB of RAM, a400 GB SSD, three 4 TB HDDs, and a network interface controller. In thiscase, the four machines can be in communication with the one or morenetworks 180 via the four network interface controllers. The fourmachines can comprise four nodes of a server cluster. The server clustercan comprise a set of physical machines that are connected together viaa network. The server cluster can be used for storing data associatedwith a plurality of virtual machines, such as backup data associatedwith different point-in-time versions of thousands of virtual machines.

The networked computing environment 100 can provide a cloud computingenvironment for one or more computing devices. Cloud computing can referto Internet-based computing, wherein shared resources, software, and/orinformation can be provided to one or more computing devices on-demandvia the Internet. The networked computing environment 100 can comprise acloud computing environment providing Software-as-a-Service (SaaS) orInfrastructure-as-a-Service (IaaS) services. SaaS can refer to asoftware distribution model in which applications are hosted by aservice provider and made available to end users over the Internet. Inone embodiment, the networked computing environment 100 can include avirtualized infrastructure that provides software, data processing,and/or data storage services to end users accessing the services via thenetworked computing environment 100. In one example, networked computingenvironment 100 can provide cloud-based work productivity orbusiness-related applications to a computing device, such as computingdevice 154.

The storage appliance 140 can comprise a cloud-based data managementsystem for backing up virtual machines and/or files within a virtualizedinfrastructure, such as virtual machines running on server 160 or filesstored on server 160 (e.g., locally stored files, files stored inmounted directories), according to some embodiments.

In some cases, networked computing environment 100 can provide remoteaccess to secure applications and files stored within data center 150from a remote computing device, such as computing device 154. The datacenter 150 can use an access control application to manage remote accessto protected resources, such as protected applications, databases, orfiles located within the data center 150. To facilitate remote access tosecure applications and files, a secure network connection can beestablished using a virtual private network (VPN). A VPN connection canallow a remote computing device, such as computing device 154, tosecurely access data from a private network (e.g., from a company fileserver or mail server) using an unsecure public network or the Internet.The VPN connection can require client-side software (e.g., running onthe remote computing device) to establish and maintain the VPNconnection. The VPN client software can provide data encryption andencapsulation prior to the transmission of secure private networktraffic through the Internet.

In some embodiments, the storage appliance 170 can manage the extractionand storage of virtual machine snapshots associated with differentpoint-in-time versions of one or more virtual machines running withinthe data center 150. A snapshot of a virtual machine can correspond witha state of the virtual machine at a particular point in time. Inresponse to a restore command from the server 160, the storage appliance170 can restore a point-in-time version of a virtual machine or restorepoint-in-time versions of one or more files located on the virtualmachine and transmit the restored data to the server 160. In response toa mount command from the server 160, the storage appliance 170 can allowa point-in-time version of a virtual machine to be mounted and allow theserver 160 to read and/or modify data associated with the point-in-timeversion of the virtual machine. To improve storage density, the storageappliance 170 can deduplicate and compress data associated withdifferent versions of a virtual machine and/or deduplicate and compressdata associated with different virtual machines. To improve systemperformance, the storage appliance 170 can first store virtual machinesnapshots received from a virtualized environment in a cache, such as aflash-based cache. The cache can also store popular data or frequentlyaccessed data (e.g., based on a history of virtual machine restorations,incremental files associated with commonly restored virtual machineversions) and current day incremental files or incremental filescorresponding with snapshots captured within the past 24 hours.

An incremental file can comprise a forward incremental file or a reverseincremental file. A forward incremental file can include a set of datarepresenting changes that have occurred since an earlier point-in-timesnapshot of a virtual machine. To generate a snapshot of the virtualmachine corresponding with a forward incremental file, the forwardincremental file can be combined with an earlier point-in-time snapshotof the virtual machine (e.g., the forward incremental file can becombined with the last full image of the virtual machine that wascaptured before the forward incremental file was captured and any otherforward incremental files that were captured subsequent to the last fullimage and prior to the forward incremental file). A reverse incrementalfile can include a set of data representing changes from a laterpoint-in-time snapshot of a virtual machine. To generate a snapshot ofthe virtual machine corresponding with a reverse incremental file, thereverse incremental file can be combined with a later point-in-timesnapshot of the virtual machine (e.g., the reverse incremental file canbe combined with the most recent snapshot of the virtual machine and anyother reverse incremental files that were captured prior to the mostrecent snapshot and subsequent to the reverse incremental file).

The storage appliance 170 can provide a user interface a web-basedinterface or a graphical user interface) that displays virtual machinebackup information such as identifications of the virtual machinesprotected and the historical versions or time machine views for each ofthe virtual machines protected. A time machine view of a virtual machinecan include snapshots of the virtual machine over a plurality of pointsin time. Each snapshot can comprise the state of the virtual machine ata particular point in time. Each snapshot can correspond with adifferent version of the virtual machine (e.g., Version 1 of a virtualmachine can correspond with the state of the virtual machine at a firstpoint in time and Version 2 of the virtual machine can correspond withthe state of the virtual machine at a second point in time subsequent tothe first point in time).

The user interface can enable an end user of the storage appliance 170(e.g., a system administrator or a virtualization administrator) toselect a particular version of a virtual machine to be restored ormounted. When a particular version of a virtual machine has beenmounted, the particular version can be accessed by a client (e.g., avirtual machine, a physical machine, or a computing device) as if theparticular version was local to the client. A mounted version of avirtual machine can correspond with a mount point directory (e.g.,/snapshots/VM5/Version23). In one example, the storage appliance 170 canrun an NFS server and make the particular version (or a copy of theparticular version) of the virtual machine accessible for reading and/orwriting. A user (e.g., virtual machine administrator or end user of avirtual machine) of the storage appliance 170 can then select theparticular version to be mounted and run an application (e.g., a dataanalytics application) using the mounted version of the virtual machine.In another example, the particular version can be mounted as an iSCSItarget.

In some embodiments, the storage appliance 140 is an external networkconnected database appliance comprising an agent 142, an application144, and a storage device 146. In some embodiments, the application 144is a software application that uses (e.g., consumes) files from a targetset of files stored locally on storage device 146, or on remote storagelocations, such as within data center 150. The agent 142 is a remoteconnection system for generating data snapshots of a target set of filesused by the storage appliance 140, and can further implementbootstrapping, upgrade, and further include backup features to transferdata from the storage appliance 140 to data center 150 via networks 180.

In some embodiments, the agent 142 can be uploaded from the data center150 and installed on the storage appliance 140. After installation onstorage appliance 140, the agent 142 can be enabled or disabled by thestorage appliance 140 over time. The agent 142 can acquire one or moreelectronic files or data snapshot information associated with the one ormore electronic files from the application 144. The data snapshotinformation can include full and/or differential snapshot data.Depending on the embodiment, the agent 142 can facilitate the server 160obtaining metadata for a file of (the target set of files accessible by)the storage appliance 140 (e.g., to determine whether the file haschanged since the last data snapshot), and the agent 142 can facilitatethe server 160 obtaining changed data detected for the file (e.g.,changed data blocks or file metadata since the last data snapshot).

The agent 142 can transfer one or more changed data blocks or changedfile metadata corresponding with the first point in time version of afile to the storage appliance 140. The one or more changed data blocksor changed file metadata can be identified by the agent 142 bygenerating and comparing fingerprints or signatures for data blocks orfile metadata of the file with previously generated fingerprints orsignatures associated with earlier point in time versions of the filecaptured prior to the first point in time. In some embodiments, theagent 142 can perform automatic upgrades or downgrades the agent 142 tobe in-sync with software changes to a plurality of nodes (e.g., nodesoperating within storage appliance 170).

In some embodiments, the agent 142 is further configured to interfacewith application 144 or storage device 146 to implement changes, such ascreating directories, database instances, reads/writes, and otheroperations to provide data management functions between the storageappliance 140 and devices within the data center 150.

FIG. 1B is a block diagram illustrating an example of a server,according to some embodiments of the present disclosure. In particular,FIG. 1B depicts an example of the server 160 in FIG. 1A. The server 160can comprise one server out of a plurality of servers that are networkedtogether within a data center. In one example, the plurality of serverscan be positioned within one or more server racks within a data center(e.g., data center 150). As depicted, the server 160 includeshardware-level components and software-level components. Thehardware-level components include one or more processors 182, one ormore memory 184, and one or more disks 185. The software-levelcomponents include a hypervisor 186, a virtualized infrastructuremanager 199, and one or more virtual machines, such as virtual machine198. The hypervisor 186 can comprise a native hypervisor or a hostedhypervisor. The hypervisor 186 can provide a virtual operating platformfor running one or more virtual machines, such as virtual machine 198.Virtual machine 198 includes a plurality of virtual hardware devicesincluding a virtual processor 192, a virtual memory 194, and a virtualdisk 195. The virtual disk 195 can comprise a file stored within the oneor more disks 185. In one example, a virtual machine can include aplurality of virtual disks, with each virtual disk of the plurality ofvirtual disks associated with a different file stored on the one or moredisks 185. Virtual machine 198 can include a guest operating system 196that runs one or more applications, such as application 197.

The virtualized infrastructure manager 199, which can correspond withthe virtualization manager 169 in FIG. 1A, can run on a virtual machineor natively on the server 160. The virtualized infrastructure manager199 can provide a centralized platform for managing a virtualizedinfrastructure that includes a plurality of virtual machines. Thevirtualized infrastructure manager 199 can manage the provisioning ofvirtual machines running within the virtualized infrastructure andprovide an interface to computing devices interacting with thevirtualized infrastructure. The virtualized infrastructure manager 199can perform various virtualized infrastructure-related tasks, such ascloning virtual machines, creating new virtual machines (e.g., newvirtual machines for new nodes of the cluster), monitoring the state ofvirtual machines, and facilitating backups of virtual machines.

In one embodiment, the server 160 can use the virtualized infrastructuremanager 199 to facilitate backups for a plurality of virtual machines(e.g., eight different virtual machines) running on the server 160. Eachvirtual machine running on the server 160 can run its own guestoperating system and its own set of applications. Each virtual machinerunning on the server 160 can store its own set of files using one ormore virtual disks associated with the virtual machine (e.g., eachvirtual machine can include two virtual disks that are used for storingdata associated with the virtual machine).

In one embodiment, a data management application running on a storageappliance, such as storage appliance 140 in FIG. 1A or storage appliance170 in FIG. 1A, can request a snapshot of a virtual machine running onserver 160. The snapshot of the virtual machine can be stored as one ormore files, with each file associated with a virtual disk of the virtualmachine. A snapshot of a virtual machine can correspond with a state ofthe virtual machine at a particular point in time. The particular pointin time can be associated with a time stamp. In one example, a firstsnapshot of a virtual machine can correspond with a first state of thevirtual machine (including the state of applications and files stored onthe virtual machine) at a first point in time (e.g., 5:30 p.m. on Jun.29, 2024) and a second snapshot of the virtual machine can correspondwith a second state of the virtual machine at a second point in timesubsequent to the first point in time (e.g., 5:30 p.m. on Jun. 30,2024).

In response to a request for a snapshot of a virtual machine at aparticular point in time, the virtualized infrastructure manager 199 canset the virtual machine into a frozen state or store a copy of thevirtual machine at the particular point in time. The virtualizedinfrastructure manager 199 can then transfer data associated with thevirtual machine (e.g., an image of the virtual machine or a portion ofthe image of the virtual machine) to the storage appliance. The dataassociated with the virtual machine can include a set of files includinga virtual disk file storing contents of a virtual disk of the virtualmachine at the particular point in time and a virtual machineconfiguration file storing configuration settings for the virtualmachine at the particular point in time. The contents of the virtualdisk file can include the operating system used by the virtual machine,local applications stored on the virtual disk, and user files (e.g.,images and word processing documents). In some cases, the virtualizedinfrastructure manager 199 can transfer a full image of the virtualmachine to the storage appliance or a plurality of data blockscorresponding with the full image (e.g., to enable a full image-levelbackup of the virtual machine to be stored on the storage appliance). Inother cases, the virtualized infrastructure manager 199 can transfer aportion of an image of the virtual machine associated with data that haschanged since an earlier point in time prior to the particular point intime or since a last snapshot of the virtual machine was taken. In oneexample, the virtualized infrastructure manager 199 can transfer onlydata associated with virtual blocks stored on a virtual disk of thevirtual machine that have changed since the last snapshot of the virtualmachine was taken. In one embodiment, the data management applicationcan specify a first point in time and a second point in time and thevirtualized infrastructure manager 199 can output one or more virtualdata blocks associated with the virtual machine that have been modifiedbetween the first point in time and the second point in time.

In some embodiments, the server 160 or the hypervisor 186 cancommunicate with a storage appliance, such as storage appliance 140 inFIG. 1A or storage appliance 170 in FIG. 1A, using a distributed filesystem protocol such as Network File System (NFS) Version 3. Thedistributed file system protocol can allow the server 160 or thehypervisor 186 to access, read, write, or modify files stored on thestorage appliance as if the files were locally stored on the server 160.The distributed file system protocol can allow the server 160 or thehypervisor 186 to mount a directory or a portion of a file systemlocated within the storage appliance 140. For example, the storageappliance 140 can include a standalone host of a database, where theserver 160 mounts the database directories as if the files were locallystored on server 160. Further, the server 160 can function as a backupdevice for storage appliance 140 by backing up data in the mounteddirectories in a distributed database within data center 150, such as acluster of nodes in storage appliance 170.

FIG. 1C is a block diagram illustrating an example of a storageappliance, according to some embodiments of the present disclosure. Inparticular, FIG. 1C depicts an example of the storage appliance 170 inFIG. 1A. The storage appliance 170 can comprise a plurality of physicalmachines that can be grouped together and presented as a singlecomputing system. Each physical machine of the plurality of physicalmachines can comprise a node in a cluster (e.g., a failover cluster). Inone example, the storage appliance 170 can be positioned within a serverrack within a data center. As depicted, the storage appliance 170includes hardware-level components and software-level components. Thehardware-level components include one or more physical machines, such asphysical machine 120 and physical machine 130. The physical machine 120includes a network interface 121, processor 122, memory 123, and disk124 all in communication with each other. Processor 122 allows physicalmachine 120 to execute computer-readable instructions stored in memory123 to perform processes described herein. Disk 124 can include a harddisk drive and/or a solid-state drive. The physical machine 130 includesa network interface 131, processor 132, memory 133, and disk 134 all incommunication with each other. Processor 132 allows physical machine 130to execute computer-readable instructions stored in memory 133 toperform processes described herein. Disk 134 can include a hard diskdrive and/or a solid-state drive. In some cases, disk 134 can include aflash-based SSD or a hybrid HDD/SSD drive. In one embodiment, thestorage appliance 170 can include a plurality of physical machinesarranged in a cluster (e.g., eight machines in a cluster). Each of theplurality of physical machines can include a plurality of multi-coreCPUs, 128 GB of RAM, a 500 GB SSD, four 4 TB HDDs, and a networkinterface controller.

In some embodiments, the plurality of physical machines can be used toimplement a cluster-based network file server. The cluster-based networkfile server may neither require nor use a front-end load balancer. Oneissue with using a front-end load balancer to host the IP address forthe cluster-based network file server and to forward requests to thenodes of the cluster-based network file server is that the front-endload balancer comprises a single point of failure for the cluster-basednetwork file server. In some cases, the file system protocol used by aserver, such as server 160 in FIG. 1A, or a hypervisor, such ashypervisor 186 in FIG. 1B, to communicate with the storage appliance 170may not provide a failover mechanism (e.g., NFS Version 3). In the casethat no failover mechanism is provided on the client side, thehypervisor may not be able to connect to a new node within a cluster inthe event that the node connected to the hypervisor fails.

In some embodiments, each node in a cluster can be connected to eachother via a network and can be associated with one or more IP addresses(e.g., two different IP addresses can be assigned to each node). In oneexample, each node in the cluster can be assigned a permanent IP addressand a floating IP address and can be accessed using either the permanentIP address or the floating IP address. In this case, a hypervisor, suchas hypervisor 186 in FIG. 1B, can be configured with a first floating IPaddress associated with a first node in the cluster. The hypervisor 186can connect to the cluster using the first floating IP address. In oneexample, the hypervisor 186 can communicate with the cluster using theNFS Version 3 protocol. Each node in the cluster can run a VirtualRouter Redundancy Protocol (VRRP) daemon. A daemon can comprise abackground process. Each VRRP daemon can include a list of all floatingIP addresses available within the cluster. In the event that the firstnode associated with the first floating IP address fails, one of theVRRP daemons can automatically assume or pick up the first floating IPaddress if no other VRRP daemon has already assumed the first floatingIP address. Therefore, if the first node in the cluster fails orotherwise goes down, then one of the remaining VRRP daemons running onthe other nodes in the cluster can assume the first floating IP addressthat is used by the hypervisor 186 for communicating with the cluster.

In order to determine which of the other nodes in the cluster willassume the first floating IP address, a VRRP priority can beestablished. In one example, given a number (N) of nodes in a clusterfrom node(0) to node(N−1), for a floating IP address (i), the VRRPpriority of node(j) can be (j−i) modulo N. In another example, given anumber (N) of nodes in a cluster from node(0) to node(N−1), for afloating IP address (i), the VRRP priority of node(j) can be (i−j)modulo N. In these cases, node(j) will assume floating IP address (i)only if its VRRP priority is higher than that of any other node in thecluster that is alive and announcing itself on the network. Thus, if anode fails, then there can be a clear priority ordering for determiningwhich other node in the cluster will take over the failed node'sfloating IP address.

In some cases, a cluster can include a plurality of nodes and each nodeof the plurality of nodes can be assigned a different floating IPaddress. In this case, a first hypervisor can be configured with a firstfloating IP address associated with a first node in the cluster, asecond hypervisor can be configured with a second floating IP addressassociated with a second node in the cluster, and a third hypervisor canbe configured with a third floating IP address associated with a thirdnode in the cluster.

As depicted in FIG. 1C, the software-level components of the storageappliance 170 can include data management system 102, a virtualizationinterface 104, a distributed job scheduler 108, a distributed metadatastore 110, a distributed file system 112, and one or more virtualmachine search indexes, such as virtual machine search index 106. In oneembodiment, the software-level components of the storage appliance 170can be run using a dedicated hardware-based appliance. In anotherembodiment, the software-level components of the storage appliance 170can be run from the cloud (e.g., the software-level components can beinstalled on a cloud service provider).

In some cases, the data storage across a plurality of nodes in a cluster(e.g., the data storage available from the one or more physicalmachines) can be aggregated and made available over a single file systemnamespace (e.g., /snapshots/). A directory for each virtual machineprotected using the storage appliance 170 can be created (e.g., thedirectory for Virtual Machine A can be/snapshots/VM_A). Snapshots andother data associated with a virtual machine can reside within thedirectory for the virtual machine. In one example, snapshots of avirtual machine can be stored in subdirectories of the directory (e.g.,a first snapshot of Virtual Machine A can reside in /snapshots/VM_A/s1/and a second snapshot of Virtual Machine A can reside in/snapshots/VM_A/s2/).

The distributed file system 112 can present itself as a single filesystem, in which as new physical machines or nodes are added to thestorage appliance 170, the cluster can automatically discover theadditional nodes and automatically increase the available capacity ofthe file system 112 for storing files and other data. Each file storedin the distributed file system 112 can be partitioned into one or morechunks or shards. Each of the one or more chunks can be stored withinthe distributed file system 112 as a separate file. The files storedwithin the distributed file system 112 can be replicated or mirroredover a plurality of physical machines, thereby creating a load-balancedand fault-tolerant distributed file system 112. In one example, storageappliance 170 can include ten physical machines arranged as a failovercluster and a first file corresponding with a snapshot of a virtualmachine (e.g., /snapshots/VM_A/s1/s1.full) can be replicated and storedon three of the ten machines.

The distributed metadata store 110 can include a distributed databasemanagement system that provides high availability without a single pointof failure. In one embodiment, the distributed metadata store 110 cancomprise a database, such as a distributed document-oriented database.The distributed metadata store 110 can be used as a distributed keyvalue storage system. In one example, the distributed metadata store 110can comprise a distributed NoSQL key value store database. In somecases, the distributed metadata store 110 can include a partitioned rowstore, in which rows are organized into tables or other collections ofrelated data held within a structured format within the key value storedatabase. A table (or a set of tables) can be used to store metadatainformation associated with one or more files stored within thedistributed file system 112. The metadata information can include thename of a file, a size of the file, file permissions associated with thefile, when the file was last modified, and file mapping informationassociated with an identification of the location of the file storedwithin a cluster of physical machines. In one embodiment, a new filecorresponding with a snapshot of a virtual machine can be stored withinthe distributed file system 112 and metadata associated with the newfile can be stored within the distributed metadata store 110. Thedistributed metadata store 110 can also be used to store a backupschedule for the virtual machine and a list of snapshots for the virtualmachine that are stored using the storage appliance 170.

In some cases, the distributed metadata store 110 can be used to manageone or more versions of a virtual machine. Each version of the virtualmachine can correspond with a full image snapshot of the virtual machinestored within the distributed file system 112 or an incremental snapshotof the virtual machine (e.g., a forward incremental or reverseincremental) stored within the distributed file system 112. In oneembodiment, the one or more versions of the virtual machine cancorrespond with a plurality of files. The plurality of files can includea single full image snapshot of the virtual machine and one or moreincrementals derived from the single full image snapshot. The singlefull image snapshot of the virtual machine can be stored using a firststorage device of a first type (e.g., a HDD) and the one or moreincrementals derived from the single full image snapshot can be storedusing a second storage device of a second type (e.g., an SSD). In thiscase, only a single full image needs to be stored and each version ofthe virtual machine can be generated from the single full image or thesingle full image combined with a subset of the one or moreincrementals. Furthermore, each version of the virtual machine can begenerated by performing a sequential read from the first storage device(e.g., reading a single file from a HDD) to acquire the full image and,in parallel, performing one or more reads from the second storage device(e.g., performing fast random reads from an SSD) to acquire the one ormore incrementals.

The distributed job scheduler 108 can be used for scheduling backup jobsthat acquire and store virtual machine snapshots for one or more virtualmachines over time. The distributed job scheduler 108 can follow abackup schedule to back up an entire image of a virtual machine at aparticular point in time or one or more virtual disks associated withthe virtual machine at the particular point in time. In one example, thebackup schedule can specify that the virtual machine be backed up at asnapshot capture frequency, such as every two hours or every 24 hours,Each backup job can be associated with one or more tasks to be performedin a sequence. Each of the one or more tasks associated with a job canbe run on a particular node within a cluster. In some cases, thedistributed job scheduler 108 can schedule a specific job to be run on aparticular node based on data stored on the particular node. Forexample, the distributed job scheduler 108 can schedule a virtualmachine snapshot job to be run on a node in a cluster that is used tostore snapshots of the virtual machine in order to reduce networkcongestion.

The distributed job scheduler 108 can comprise a distributedfault-tolerant job scheduler, in which jobs affected by node failuresare recovered and rescheduled to be run on available nodes. In oneembodiment, the distributed job scheduler 108 can be fully decentralizedand implemented without the existence of a master node. The distributedjob scheduler 108 can run job scheduling processes on each node in acluster or on a plurality of nodes in the cluster. In one example, thedistributed job scheduler 108 can run a first set of job schedulingprocesses on a first node in the cluster, a second set of job schedulingprocesses on a second node in the cluster, and a third set of jobscheduling processes on a third node in the cluster. The first set ofjob scheduling processes, the second set of job scheduling processes,and the third set of job scheduling processes can store informationregarding jobs, schedules, and the states of jobs using a metadatastore, such as distributed metadata store 110. In the event that thefirst node running the first set of job scheduling processes fails(e.g., due to a network failure or a physical machine failure), thestates of the jobs managed by the first set of job scheduling processesmay fail to be updated within a threshold period of time (e.g., a jobmay fail to be completed within 30 seconds or within minutes from beingstarted). In response to detecting jobs that have failed to be updatedwithin the threshold period of time, the distributed job scheduler 108can undo and restart the failed jobs on available nodes within thecluster.

The job scheduling processes running on at least a plurality of nodes ina cluster (e.g., on each available node in the cluster) can manage thescheduling and execution of a plurality of jobs. The job schedulingprocesses can include run processes for running jobs, cleanup processesfor cleaning up failed tasks, and rollback processes for rolling-back orundoing any actions or tasks performed by failed jobs. In oneembodiment, the job scheduling processes can detect that a particulartask for a particular job has failed and in response can perform acleanup process to clean up or remove the effects of the particular taskand then perform a rollback process that processes one or more completedtasks for the particular job in reverse order to undo the effects of theone or more completed tasks. Once the particular job with the failedtask has been undone, the job scheduling processes can restart theparticular job on an available node in the cluster.

The distributed job scheduler 108 can manage a job in which a series oftasks associated with the job are to be performed atomically (i.e.,partial execution of the series of tasks is not permitted). If theseries of tasks cannot be completely executed or there is any failurethat occurs to one of the series of tasks during execution (e.g., a harddisk associated with a physical machine fails or a network connection tothe physical machine fails), then the state of a data management systemcan be returned to a state as if none of the series of tasks were everperformed. The series of tasks can correspond with an ordering of tasksfor the series of tasks and the distributed job scheduler 108 can ensurethat each task of the series of tasks is executed based on the orderingof tasks. Tasks that do not have dependencies with each other can beexecuted in parallel.

In some cases, the distributed job scheduler 108 can schedule each taskof a series of tasks to be performed on a specific node in a cluster. Inother cases, the distributed job scheduler 108 can schedule a first taskof the series of tasks to be performed on a first node in a cluster anda second task of the series of tasks to be performed on a second node inthe cluster. In these cases, the first task can have to operate on afirst set of data (e.g., a first file stored in a file system) stored onthe first node and the second task can have to operate on a second setof data (e.g., metadata related to the first file that is stored in adatabase) stored on the second node. In some embodiments, one or moretasks associated with a job can have an affinity to a specific node in acluster.

In one example, if the one or more tasks require access to a databasethat has been replicated on three nodes in a cluster, then the one ormore tasks can be executed on one of the three nodes. In anotherexample, if the one or more tasks require access to multiple chunks ofdata associated with a virtual disk that has been replicated over fournodes in a cluster, then the one or more tasks can be executed on one ofthe four nodes. Thus, the distributed job scheduler 108 can assign oneor more tasks associated with a job to be executed on a particular nodein a cluster based on the location of data required to be accessed bythe one or more tasks.

In one embodiment, the distributed job scheduler 108 can manage a firstjob associated with capturing and storing a snapshot of a virtualmachine periodically (e.g., every 30 minutes). The first job can includeone or more tasks, such as communicating with a virtualizedinfrastructure manager, such as the virtualized infrastructure manager199 in FIG. 1B, to create a frozen copy of the virtual machine and totransfer one or more chunks (or one or more files) associated with thefrozen copy to a storage appliance, such as storage appliance 170 inFIG. 1A. The one or more tasks can also include generating metadata forthe one or more chunks, storing the metadata using the distributedmetadata store 110, storing the one or more chunks within thedistributed file system 112, and communicating with the virtualizedinfrastructure manager 199 that the frozen copy of the virtual machinecan be unfrozen or released from a frozen state. The metadata for afirst chunk of the one or more chunks can include information specifyinga version of the virtual machine associated with the frozen copy, a timeassociated with the version (e.g., the snapshot of the virtual machinewas taken at 5:30 p.m. on Jun. 29, 2024), and a file path to where thefirst chunk is stored within the distributed file system 112 (e.g., thefirst chunk is located at snapshots/VM_B/s1/s1.chunk1). The one or moretasks can also include deduplication, compression (e.g., using alossless data compression algorithm such as LZ4 or LZ77), decompression,encryption (e.g., using a symmetric key algorithm such as Triple DES orAES-256), and decryption-related tasks.

The virtualization interface 104 can provide an interface forcommunicating with a virtualized infrastructure manager managing avirtualization infrastructure, such as virtualized infrastructuremanager 199 in FIG. 1B, and requesting data associated with virtualmachine snapshots from the virtualization infrastructure. Thevirtualization interface 104 can communicate with the virtualizedinfrastructure manager using an API for accessing the virtualizedinfrastructure manager (e.g., to communicate a request for a snapshot ofa virtual machine). In this case, storage appliance 170 can request andreceive data from a virtualized infrastructure without requiring agentsoftware to be installed or running on virtual machines within thevirtualized infrastructure. The virtualization interface 104 can requestdata associated with virtual blocks stored on a virtual disk of thevirtual machine that have changed since a last snapshot of the virtualmachine was taken or since a specified prior point in time. Therefore,in some cases, if a snapshot of a virtual machine is the first snapshottaken of the virtual machine, then a full image of the virtual machinecan be transferred to the storage appliance. However, if the snapshot ofthe virtual machine is not the first snapshot taken of the virtualmachine, then only the data blocks of the virtual machine that havechanged since a prior snapshot was taken can be transferred to thestorage appliance.

The virtual machine search index 106 can include a list of files thathave been stored using a virtual machine and a version history for eachof the files in the list. Each version of a file can be mapped to theearliest point-in-time snapshot of the virtual machine that includes theversion of the file or to a snapshot of the virtual machine thatincludes the version of the file (e.g., the latest point-in-timesnapshot of the virtual machine that includes the version of the file).In one example, the virtual machine search index 106 can be used toidentify a version of the virtual machine that includes a particularversion of a file (e.g., a particular version of a database, aspreadsheet, or a word processing document). In some cases, each of thevirtual machines that are backed up or protected using storage appliance170 can have a corresponding virtual machine search index.

In one embodiment, as each snapshot of a virtual machine is ingested,each virtual disk associated with the virtual machine is parsed in orderto identify a file system type associated with the virtual disk and toextract metadata (e.g., file system metadata) for each file stored onthe virtual disk. The metadata can include information for locating andretrieving each file from the virtual disk. The metadata can alsoinclude a name of a file, the size of the file, the last time at whichthe file was modified, and a content checksum for the file. Each filethat has been added, deleted, or modified since a previous snapshot wascaptured can be determined using the metadata (e.g., by comparing thetime at which a file was last modified with a time associated with theprevious snapshot). Thus, for every file that has existed within any ofthe snapshots of the virtual machine, a virtual machine search index canbe used to identify when the file was first created (e.g., correspondingwith a first version of the file) and at what times the file wasmodified (e.g., corresponding with subsequent versions of the file).Each version of the file can be mapped to a particular version of thevirtual machine that stores that version of the file.

In some cases, if a virtual machine includes a plurality of virtualdisks, then a virtual machine search index can be generated for eachvirtual disk of the plurality of virtual disks. For example, a firstvirtual machine search index can catalog and map files located on afirst virtual disk of the plurality of virtual disks and a secondvirtual machine search index can catalog and map files located on asecond virtual disk of the plurality of virtual disks. In this case, aglobal file catalog or a global virtual machine search index for thevirtual machine can include the first virtual machine search index andthe second virtual machine search index. A global file catalog can bestored for each virtual machine backed up by a storage appliance withina file system, such as distributed file system 112 in FIG. 1C.

The data management system 102 may comprise an application running onthe storage appliance (e.g., storage appliance 170) that manages andstores one or more snapshots of a virtual machine. In one example, thedata management system 102 may comprise a highest-level layer in anintegrated software stack running on the storage appliance. For someembodiments, the data management system 102 comprises a discontiguousfileset engine 114, which can enable the data management system 102 toperform various methodologies described herein, such as classifying eachfile in a target set of files into one of two or more discontiguousfilesets based on one or more factors, such as frequency of file changeor purpose of existence. The integrated software stack may include thedata management system 102, the virtualization interface 104, thedistributed job scheduler 108, the distributed metadata store 110, andthe distributed file system 112.

In some cases, the integrated software stack may run on other computingdevices, such as a server or computing device 154 in FIG. 1A. The datamanagement system 102 may use the virtualization interface 104, thedistributed job scheduler 108, the distributed metadata store 110, andthe distributed file system 112 to manage and store one or moresnapshots of a virtual machine. Each snapshot of the virtual machine maycorrespond with a point-in-time version of the virtual machine. The datamanagement system 102 may generate and manage a list of versions for thevirtual machine. Each version of the virtual machine may map to orreference one or more chunks and/or one or more files stored within thedistributed file system 112. Combined together, the one or more chunksand/or the one or more files stored within the distributed file system112 may comprise a full image of the version of the virtual machine.

FIGS. 2 through 7 are flowcharts illustrating example methods ofmanaging files according to categories, in accordance with someembodiments. It will be understood that example methods described hereinmay be performed by one or more machines (e.g., physical or virtualmachines), such as a computing device executing instructions associatedwith an alert system described herein with respect to some embodiments.Additionally, example methods described herein may be implemented in theform of executable instructions stored on a computer-readable medium orin the form of electronic circuitry. For instance, the operations of amethod 200 of FIG. 2 may be represented by executable instructions that,when executed by a processor of a machine (e.g., physical machine 120),cause the computing device to perform the method 200. Depending on theembodiment, an operation of an example method described herein may berepeated in different ways or involve intervening operations not shown.Though the operations of example methods may be depicted and describedin a certain order, the order in which the operations are performed mayvary among embodiments, including performing certain operations inparallel.

Referring now to FIG. 2, the flowchart illustrates the example method200 for generating data snapshots of one or more files according tofileset classes, in accordance with some embodiments. The method 200 asillustrated begins with operation 205 generating a full data snapshot ofa target set of files being data protected. For some embodiments,operation 205 is performed as part of initially starting data protectionof the target set of files.

The method 200 continues with operation 210 generating one or moreincremental data snapshots of the target set of files over time. Asnoted herein, when data protection initially starts for the target setof files, each of the files can be associated with an initial filesetclass (e.g., one of fileset classes A, B, or C, such as the oneassociated with an SLA), which can determine the initial frequency atwhich incremental data snapshots are generated for the target set offiles. Accordingly, the one or more incremental data snapshots can begenerated over time according to this initial frequency of the initialfileset class. According to some embodiments described herein, theinitial frequency for some of the files of the target set of files canchange once those files are associated with another fileset class (e.g.,a fileset class other than the initial fileset class). For example, asdescribed herein, after a sufficient sample size of incremental datasnapshots have been generated for the target set of files, the frequencyof data content change of files in the target set of files can bedetermined, the frequency of data content change of the files candetermine their respective ranks, their respective ranks can determinetheir respective associations with fileset classes, and their respectiveassociations with fileset classes can determine their respectiveincremental data snapshot generation frequency.

For some embodiments, generation of a single incremental data snapshotof a given fileset (e.g., all files of a target set of files or of adiscontiguous fileset) comprises obtaining metadata for each individualfile in the given fileset to detect whether the individual file (e.g.,its data content) has changed. The metadata for a given file cancomprise one or more attributes (e.g., mtime, size, etc.) of the givenfile that can determine whether data content (e.g., a data block or filemetadata) of the file has changed. The metadata for a given file can beobtained, for example, by way of a stat( ) system call or the like.

As described herein, frequency of change of a file is determined basedon a sample of prior incremental data snapshots. According to someembodiments, each file has an associated change count (e.g., stored inits metadata) that can be updated (e.g., incremented by one) each timean incremental data snapshot process determines that the file haschanged (e.g., based on file metadata scan). For some embodiments, thechange count of each file is reset (e.g., to 0) after a specific samplespace (e.g., sample space of 10 incremental data snapshots) has ended.

The method 200 continues with operation 215 determining whether aminimal number of data snapshots (e.g., incremental data snapshots)exists for determining change frequency of files (in the target set offiles) for file ranking purposes. For instance, for some embodiments,the sufficient minimal sample size can comprise ten incremental datasnapshots, where the ten incremental data snapshots do not necessarilyneed to be generated consecutively (e.g., a full data snapshot may ormay not be generated between generation of two of the ten incrementaldata snapshots). For some embodiments, the sample size for determiningfrequency of change of a given file is limited to the last x number(e.g., last ten) of incremental data snapshots. Alternatively, for someembodiments, the sample size for determining frequency of change of agiven file comprises all prior incremental data snapshots.

The method 200 continues with operation 220 determining a set of ranksfor the target set of files, where the set of ranks can include acorresponding rank for each file in the target set of files. Asdescribed herein, rank of a file can be determined based on one or morefactors (ranking factors), such as frequency of change in data contentof the file (e.g., one or more incremental data snapshots), purpose ofexistence of the file (e.g., significance rank of software applicationsthat use the file), or some combination thereof.

For instance, a rank of a given file can be determined based on one ormore of the following. Frequency of change of a specific file X (of thetarget set of files) can be designated as F(X), where (Equation 1):

${F(X)} = \frac{\begin{matrix}\left( {{number}\mspace{14mu}{of}\mspace{14mu}{times}\mspace{14mu}{change}\mspace{14mu}{detected}\mspace{14mu}{for}\mspace{14mu} X} \right. \\\left. {{over}\mspace{14mu}{set}\mspace{14mu}{of}\mspace{14mu}{incremental}\mspace{14mu}{data}\mspace{14mu}{snapshots}} \right)\end{matrix}}{\begin{matrix}\left( {{total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{incremental}\mspace{14mu}{data}\mspace{14mu}{snapshots}} \right. \\\left. {{in}{\mspace{11mu}\;}{set}\mspace{14mu}{of}\mspace{14mu}{incremental}\mspace{14mu}{data}\mspace{14mu}{snapshots}} \right)\end{matrix}}$For some embodiments, a change count maintained for the specific file X(e.g., maintained in its metadata) can be used to determine the numberof times a file change is detected for file X over the set ofincremental data snapshots. For some embodiments, the specific file X isreset (e.g., to 0) after a specific sample space (e.g., sample space of10 incremental data snapshots) has ended. The set of incremental datasnapshots can include those existing incremental data snapshots (e.g.,all prior incremental data snapshots or only the last ten incrementaldata snapshots) that are being considered for ranking the file X.

Percentage frequency of change for a specific file X (of the target setof files) can be designed as PF(X), where (Equation 2):PF(X)=F(X)×100The percentage frequency of change for the specific file X can representthe overall change to the file X over the set of incremental datasnapshots.

Percentile frequency of change for a specific file X (of the target setof files) can be designed as PT(X), where (Equation 3):

${{PT}(X)} = {\frac{\begin{matrix}\left( {{number}\mspace{14mu}{of}\mspace{14mu}{other}\mspace{14mu}{files}\mspace{14mu}{within}\mspace{14mu}{target}\mspace{14mu}{set}\mspace{14mu}{of}\mspace{14mu}{files}} \right. \\\left. {{having}\mspace{14mu} a\mspace{14mu}{frequency}\mspace{14mu}{less}\mspace{14mu}{than}\mspace{14mu}{F(X)}} \right)\end{matrix}}{\left( {{total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{files}\mspace{14mu}{in}\mspace{14mu}{target}\mspace{14mu}{set}\mspace{14mu}{of}\mspace{14mu}{files}} \right)} \times 100}$The percentile frequency of change for the specific file X can representthe overall change to the file X (over the set of incremental datasnapshots being considered) with respect to (e.g., relative or incomparison to) all other files within the target set of files.

Significance rank of a software application consuming a specific file X(of the target set of files) can be represented as S(X). Depending onthe embodiment, the significance rank of a particular softwareapplication can range from 1 (e.g., most important) to a number s, wheres can depend on the implementation (e.g., s can represent the number ofsoftware applications being considered). For instance, a databasesoftware application can have an assigned significance rank of 1, and aweb server software application can have an assigned significance rankof 2. The significance rank of a software application can be userdefined (e.g., defined by a data management administrator in the datamanagement system 102). The last value s can represent the lowestsignificance rank value possible, and can be defined as a default value(e.g., assigned where the software application that consumes thatspecific file X is unknown or is of the least importance).

Based on the foregoing, the rank of a specific file X (of the target setof files) can be designated as R(X), where (Equation 4):

${R({file})} = {\left( \frac{{PF}(X)}{2} \right) + \left( \frac{{PT}(X)}{4} \right) + \left( {W - \left( \frac{S(X)}{\left( {N + 1} \right)} \right)} \right)}$where W can equal a constant factor (e.g., one determined to be suitablefor an implementation, such as 25) and N represents the total number ofsoftware applications having a significance rank (e.g., in the datamanagement system 102). For some embodiments, a rank generated by theequation above is subject to an upper bound of 100. Depending on theembodiment, the equation above can be modified to generate a rank thatis subject to a lower bound value or a different upper bound value(e.g., one that is less than or more than 100).

The method 200 continues with operation 225 determining association ofeach file in the target set of files to a fileset class based on the setof ranks determined by operation 220. For some embodiments, each filesetclass is associated with a range of ranks (e.g., exclusive ranges ofranks). A given file (in the target set of files) can be associated witha given fileset class in response to a rank of the given file fallingwithin the range of ranks associated with the given fileset class. Forexample, as described herein, the possible fileset classes can comprisefileset class A, fileset class B, and fileset class C, where eachfileset class can be associated with (e.g., represent) a differentdiscontiguous fileset. The fileset class A can be associated with ranksranging from 50 to 100, the fileset class B can be associated with ranksranging from 10 to less than 50, and the fileset class C can beassociated with ranks less than 10. Fileset class A can be associatedwith files subject to frequent generation of incremental data snapshots,such as log files, database files, or software application-generatedfiles. Fileset class B can be associated with files subject to lessfrequent generation of incremental data snapshots, such as softwareapplication configuration files. Fileset class C can be associated withfiles subject to even less frequent generation of incremental datasnapshots, such as library files (e.g., files in /lib or /usr/local/libdirectories) or software application binary files.

For some embodiments, each file in the target set of files isperiodically classified (or re-classified) to a fileset class byperiodically determining (or re-determining) a rank for each file andassociating each file with a fileset class based on the newly determinedrank (which may or may not remain unchanged for a given file).Accordingly, the newly determined rank for a given file can be demotedor promoted from being associated with its current fileset class toanother fileset class, or remain associated with its current filesetclass. The periodic classification can be performed, for example, priorto, during, or after generation of an incremental data snapshot.

The method 200 continues with operation 230 setting or adjusting (e.g.,modifying) a data snapshot policy with respect to a subset of files, ofthe target set of files, based on a file set class associated with thesubset of files. For example, fileset class B can be associated with abase frequency for generating incremental data snapshots (e.g., asdefined by a SLA), fileset class A can be associated with a frequencythat is a constant higher than the base frequency, and fileset class Ccan be associated with a frequency that is a constant y lower than thebase frequency. Accordingly, the data snapshot policy can be adjustedsuch that: files (of the target set of files) associated with a filesetclass B have data snapshots (e.g., incremental data snapshots) generatedat the base frequency; files (of the target set of files) associatedwith a fileset class A have data snapshots (e.g., incremental datasnapshots) generated at a frequency that is x higher than the basefrequency; and files (of the target set of files) associated with afileset class C have data snapshots (e.g., incremental data snapshots)generated at a frequency that is y lower than the base frequency. Forsome embodiments, the data snapshot policy is periodically set oradjusted based on the periodic classification of files with filesetclasses.

Referring now to FIG. 3, the flowchart illustrates the example method300 for performing a data management operation on one or more filesaccording to fileset classes, in accordance with some embodiments.Depending on the embodiment, some or all of the method 300 is performedas part of a data snapshot generation process (e.g., incremental datasnapshot process). The method 300 as illustrated begins with operation305 identifying a target set of files, stored on a filesystem, to beranked. For some embodiments, operation 305 comprises identifying thetarget set of files based on a user input (e.g., from an end user of avirtual machine or a data management administrator) that identifies adirectory (e.g., root directory of a virtual machine) for datamanagement (e.g., protection by data snapshots). The user can, forexample identify the directory by way of a graphical user interface,such as a web-based graphical user interface. Additionally, for someembodiments, identifying the target set of files to be ranked comprisesidentifying each file stored on the file system that meets or exceeds aminimal sample size for determining frequency of file data change. Forinstance, for data snapshot generation purposes, the target set of filesmay not be identified by operation 305 until the files in the target sethave been examined (for data change) a minimum number of times by a datasnapshot process (e.g., for generating an incremental data snapshot).

The method 300 continues with operation 310 determining a set of fileranks for the target set of files identified at operation 305. For someembodiments, operation 310 comprises ranking each file in the target setof files based on a set of ranking factors, which can include afrequency of file data change or an association of the file with asoftware application (e.g., that uses or consumes the file). Forinstance, operation 310 can comprise determining how many times thegiven file changed over a set of prior data snapshots (e.g., todetermine level of change between prior data snapshots). For someembodiments, this determination for the given file is performed over apredetermined number of prior data snapshots (e.g., ten priorincremental data snapshots). With respect to a software application, thesoftware application can be associated with a significance rank (e.g.,user defined or known by a data management system), which can beconsidered in determining the rank of the file.

The method 300 continues with operation 315 associating each individualfile, in the target set of files, to a given fileset class from aplurality of fileset classes. For some embodiments, an individual fileis associated to a given fileset class based on a respective file rank(from the set of file ranks determined at operation 310) for theindividual file and based on a given range of ranks associated with thegiven fileset class (e.g., the respective file rank falls within thegiven range of ranks). As described herein, each fileset class of theplurality of fileset classes can be associated with a different range ofranks (e.g., range of ranks that do not overlap). For some embodiments,a file in the target set of files is only associated with a singlefileset class at a given time. Additionally, for some embodiments, eachfile in the target set of files can be initially associated (e.g., priorto operation 315) with an initial or default fileset class from theplurality of fileset classes.

The method 300 continues with operation 320 causing performance of adata management operation with respect to a particular file in thetarget set of files. For some embodiments, operation 320 comprisescausing performance of the data management operation with respect to theparticular file based on an association of the particular file to aparticular fileset class from the plurality of fileset classes.Additionally, for some embodiments, operation 320 comprises adjusting,based on the association of the particular file to the particularfileset class, policy data that determines performance of the datamanagement operation with respect to at least the particular file. Asdescribed herein, for some embodiments, the data management operationcaused to be performed comprises performing a data snapshot generationprocess (also referred to herein as a data snapshot process) withrespect to the particular file in the target set of files.

Referring now to FIG. 4, the flowchart illustrates the example method400 for performing a data management operation on one or more filesaccording to fileset classes, in accordance with some embodiments.Depending on the embodiment, some or all of the method 400 is performedas part of a data snapshot generation process (e.g., incremental datasnapshot process). The method 400 as illustrated begins with operation405, which according to some embodiments is similar to operation 305described above with respect to the method 300 described above withrespect to FIG. 3.

The method 400 continues with operation 410 where, for each incrementaldata snapshot generated, operation 410 updates (e.g., increments by avalue, such as one) a change count for each file determined to havechanged for the incremental data snapshot. Depending on the embodiment,with respect to a data snapshot process (e.g., incremental data snapshotprocess) performed on a given file, the change count of the given filecan be updated as part of the data snapshot process, can be performedafter the data snapshot process has completed, or can be performed justbefore the data snapshot process is performed (e.g., performed at afrequency based on a data snapshot policy associated with the givenfile).

The method 400 continues with operation 415 determining a set of fileranks for the target set of files identified at operation 405. For someembodiments, operation 415 comprises performing operations 430 through440 with respect to each given file in the target set of files. Asillustrated, operation 430 comprises determining a number of times agiven file in the target set of files has changed over a set of priordata snapshot (e.g., existing incremental data snapshots). As describedherein, for some embodiments, determining how many times a given filechanged over a set of data snapshots can be based on a change count ofthe given file (as updated by operation 410). For instance, operation430 can use Equation 1, Equation 2, or both as described herein todetermine how many times the given file changed over the set of datasnapshots. Operation 435 comprises determining how frequently the givenfile has changed with respect to another file (e.g., relative or incomparison to all other files) in the target set of files. For instance,operation 435 can use Equation 3 as described herein to determine howfrequently the given file has changed with respect to all other files inthe target set of files.

Operation 440 comprises determining a rank of the given file based atleast on one of: the number of times the given file has changed over aset of prior data snapshot (as determined by operation 430), howfrequently the given file has changed with respect to another file inthe target set of files (as determined by operation 435), and asignificance rank of a software application associated with (e.g., thatuses or consumes) the given file.

For instance, operation 440 can use Equation 4 as described herein todetermine the rank of the given file based on the number of times thegiven file has changed over a set of prior data snapshots, howfrequently the given file has changed with respect to all other files inthe target set of files, and significance rank of a software applicationassociated with the given file.

The method 400 continues with operations 420 and 425, which arerespectively similar to operations 315 and 320 of the method 300described above with respect to FIG. 3.

Referring now to FIG. 5, the flowchart illustrates the example method500 for generating data snapshots of one or more files according filesetclasses, in accordance with some embodiments. The method 500 asillustrated begins with operation 505 determining whether a plurality ofexisting data snapshots comprises a minimal number of data snapshots forthe target set of files.

The method 500 continues with operation 510 periodically classifyingeach file in the target set of files to one of a plurality of filesetclasses based on a set of file ranks for the target set of files. Forsome embodiments, each fileset class of the plurality of fileset classesis associated with a different time interval in a plurality of snapshotgeneration time intervals. For some embodiments, operation 510 comprisesperiodically determining the set of file ranks of the target set offiles by ranking each file in the target set of files based on a set ofranking factors, where the set of ranking factors comprises a frequencyof file data change. Additionally, for some embodiments, operation 510further comprises periodically associating each file in the target setof files to a given fileset class (from the plurality of filesetclasses) based on a respective file rank from the set of file ranks andbased on a given range of ranks associated with the given fileset class.As described herein, each fileset class of the plurality of filesetclasses can be associated with a different range of ranks. Each file inthe target set of files can be initially associated (e.g., prior tooperation 510) with a same fileset class from the plurality of filesetclasses. Depending on the embodiment, the operation 510 can be performedafter generation of a new data snapshot (e.g., new incremental datasnapshot), or prior to generation of a new data snapshot.

The method 500 continues with operation 515 causing periodic generationof data snapshots (e.g., incremental data snapshots) for the target setof files based on the plurality of fileset classes and according to theplurality of snapshot generation time intervals as associated with thetarget set of files (as classified by operation 510). For example, foreach individual fileset class of the plurality of fileset classes,operation 515 can cause periodic generation of a data snapshot (e.g.,incremental data snapshot) for a given subset of files of the target setof files according to a given snapshot generation time interval (fromthe plurality of snapshot generation time intervals), where the givensubset of files and the given snapshot generation are associated withthe same individual fileset class. For some embodiments, operation 515comprises adjusting, based on the plurality of fileset classes andaccording to the plurality of snapshot generation time intervals asassociated with the target set of files, policy data that determinesperiodic generation of data snapshots (e.g., incremental data snapshots)for the target set of files.

As described herein, the plurality of fileset classes can comprise afirst fileset class (e.g., fileset class A), a second fileset class(e.g., fileset class B), and a third fileset class (e.g., fileset classC), where the plurality of snapshot generation time intervals comprisesa first snapshot generation time interval associated with the firstfileset class, a second snapshot generation time interval associatedwith the second fileset class, and a third snapshot generation timeinterval associated with the third fileset class. For some embodiments,the first snapshot generation time interval is greater than the secondsnapshot generation time interval, and the second snapshot generationtime interval is greater than the third snapshot generation timeinterval.

Referring now to FIG. 6, the flowchart illustrates the example method600 for generating data snapshots of one or more files according tofileset classes, in accordance with some embodiments. The method 600 asillustrated begins with operation 605 generating a plurality of existingdata snapshots of a target set of files stored on a file system overtime according to a defined snapshot generation time interval (e.g.,defined according to a SLA). For various embodiments, a snapshotgeneration time interval determines a data snapshot frequency.

The method 600 continues with operations 610 through 620, whichaccording to some embodiments are respectively similar to operation 505through 515 of the method 500 described above with respect to FIG. 5.

Referring now to FIG. 7, the flowchart illustrates the example method700 for generating data snapshots of one or more files of a remotecomputing device (e.g., storage appliance 140 of FIG. 1) according tofileset classes, in accordance with some embodiments. For someembodiments, the method 700 is performed by the server 160. The method700 as illustrated begins with operation 705 generating a plurality ofdata snapshots of a target set of files stored on a filesystem of aremote computing device. Depending on the embodiment, the plurality ofdata snapshots can comprise one or more full data snapshots and one ormore incremental data snapshots.

The method 700 continues with operation 710 periodically classifyingeach file in the target set of files to one of a plurality of filesetclasses based on a set of file ranks for the target set of files. Forsome embodiments, each fileset class of the plurality of fileset classesis associated with a different time interval in a plurality of snapshotgeneration time intervals. As described herein, a snapshot generationtime interval associated with a given fileset class can determine afrequency at which a data snapshot (e.g., an incremental data snapshot)is generated with respect to files associated with the given filesetclass.

The method 700 continues with operation 715 for each individual filesetclass of the plurality of fileset classes, causing periodic generationof a data snapshot (e.g., an incremental data snapshot) for a givensubset of files of the target set of files. For some embodiments, a datasnapshot for the given subset of files is periodically generatedaccording to a given snapshot generation time interval (from theplurality of snapshot generation time intervals) that is associated withthe same fileset class as the given subset of files. For someembodiments, operation 715 comprises adjusting policy data thatdetermines periodic generation of the incremental data snapshot for thegiven subset of files.

For some embodiments, operation 715 comprises performing operations 730through 740 with respect to each given file in the target set of files.As illustrated, operation 730 comprises obtaining, from the remotecomputing device, metadata for the individual file. For instance,operation 730 can comprise obtaining, from a remote agent installed onthe remote computing device, the metadata for the individual file. Forsome embodiments, the remote agent performs a system call, such as astat( ) system call, on the individual file to obtain the metadata.Accordingly, operation 730 can comprise sending a set of instructions tothe remote computing device (e.g., from the server 160 to the storageappliance 140) that causes the remote computing device to perform astat( ) system call with respect to the individual file. The remoteagent can provide the metadata to the server 160 at the request of theserver 160. As described herein, for a data snapshot generation (e.g.,an incremental data snapshot generation), each individual fileconsidered can comprise obtaining metadata of the individual file.Operation 735 comprises determining, based on the metadata (obtained atoperation 730), whether the individual file changed since a last datasnapshot. Operation 740 comprises obtaining, from the remote computingdevice, at least changed data for the individual file responsive todetermining (at operation 735) that the individual file changed sincethe last data snapshot. For some embodiments, operation 740 comprisesobtaining, from a remote agent installed on the remote computing device,the at least changed data (e.g., changed data blocks or changed filemetadata) for the individual file.

FIG. 8 is a block diagram 800 illustrating an example architecture ofsoftware 802 that can be used to implement various embodiments describedherein. FIG. 8 is merely a non-limiting example of a softwarearchitecture, and it will be appreciated that many other architecturescan be implemented to facilitate the functionality described herein. Invarious embodiments, the software 802 is implemented by hardware such asa machine 900 of FIG. 9 that includes processors 910, memory 930, andI/O components 950. In this example architecture, the software 802 canbe conceptualized as a stack of layers where each layer may provide aparticular functionality. For example, the software 802 includes layerssuch as an operating system 804, libraries 806, frameworks 808, andapplications 810. Operationally, the applications 810 invoke applicationprogramming interface (API) calls 812 through the software stack andreceive messages 814 in response to the API calls 812, consistent withsome embodiments.

In various implementations, the operating system 804 manages hardwareresources and provides common services. The operating system 804includes, for example, a kernel 820, services 822, and drivers 824. Thekernel 820 acts as an abstraction layer between the hardware and theother software layers, consistent with some embodiments. For example,the kernel 820 provides memory management, processor management (e.g.,scheduling), component management, networking, and security settings,among other functionality. The services 822 can provide other commonservices for the other software layers. The drivers 824 are responsiblefor controlling or interfacing with the underlying hardware, accordingto some embodiments. For instance, the drivers 824 can include displaydrivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers,flash memory drivers, serial communication drivers (e.g., UniversalSerial Bus (USB) drivers), WI-FI® drivers, audio drivers, powermanagement drivers, and so forth.

In some embodiments, the libraries 806 provide a low-level commoninfrastructure utilized by the applications 810. The libraries 806 caninclude system libraries 830 (e.g., C standard library) that can providefunctions such as memory allocation functions, string manipulationfunctions, mathematic functions, and the like. In addition, thelibraries 806 can include API libraries 832 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as Moving Picture Experts Group-4 (MPEG4), AdvancedVideo Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3),Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec,Joint Photographic Experts Group (JPEG or JPG), or Portable NetworkGraphics (PNG)), graphics libraries (e.g., an OpenGL framework used torender in two dimensions (2D) and three dimensions (3D) in a graphiccontent on a display), database libraries (e.g., SQLite to providevarious relational database functions), web libraries (e.g., WebKit toprovide web browsing functionality), and the like. The libraries 806 canalso include a wide variety of other libraries 834 to provide many otherAPIs to the applications 810.

The frameworks 808 provide a high-level common infrastructure that canbe utilized by the applications 810, according to some embodiments. Forexample, the frameworks 808 provide various graphic user interface (GUI)functions, high-level resource management, high-level location services,and so forth. The frameworks 808 can provide a broad spectrum of otherAPIs that can be utilized by the applications 810, some of which may bespecific to a particular operating system or platform.

In some embodiments, the applications 810 include a built-in application864 and a broad assortment of other applications such as a third-partyapplication 866. According to some embodiments, the applications 810 areprograms that execute functions defined in the programs. Variousprogramming languages can be employed to create one or more of theapplications 810, structured in a variety of manners, such asobject-oriented programming languages (e.g., Objective-C, Java, or C++)or procedural programming languages (e.g., C or assembly language). In aspecific example, the third-party application 866 (e.g., an applicationdeveloped using the ANDROID™ or IOS™ software development kit (SDK) byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™, WINDOWS® Phone, or another mobile operating system. In thisexample, the third-party application 866 can invoke the API calls 812provided by the operating system 804 to facilitate functionalitydescribed herein.

FIG. 9 illustrates a diagrammatic representation of an example machine900 in the form of a computer system within which a set of instructionsmay be executed for causing the machine to perform any one or more ofthe methodologies of various embodiments described herein. Specifically,FIG. 9 shows a diagrammatic representation of the machine 900 in theexample form of a computer system, within which instructions 916 (e.g.,software, a program, an application, an applet, an app, or otherexecutable code) for causing the machine 900 to perform any one or moreof the methodologies discussed herein may be executed. For example, theinstructions 916 may cause the machine 900 to execute the method 200 ofFIG. 2. Additionally, or alternatively, the instructions 916 mayimplement FIGS. 2-7, and so forth. The instructions 916 transform thegeneral, non-programmed machine 900 into a particular machine 900programmed to carry out the described and illustrated functions in themanner described. In alternative embodiments, the machine 900 operatesas a standalone device or may be coupled (e.g., networked) to othermachines. In a networked deployment, the machine 900 may operate in thecapacity of a server machine or a client machine in a server-clientnetwork environment, or as a peer machine in a peer-to-peer (ordistributed) network environment. The machine 900 may comprise, but notbe limited to, a server computer, a client computer, a personal computer(PC), a tablet computer, a laptop computer, a netbook, a set-top box(STB), a PDA, an entertainment media system, a cellular telephone, asmart phone, a mobile device, a wearable device (e.g., a smart watch), asmart home device (e.g., a smart appliance), other smart devices, a webappliance, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 916, sequentially orotherwise, that specify actions to be taken by the machine 900. Further,while only a single machine 900 is illustrated, the term “machine” shallalso be taken to include a collection of machines 900 that individuallyor jointly execute the instructions 916 to perform any one or more ofthe methodologies discussed herein.

The machine 900 may include processors 910, memory 930, and I/Ocomponents 950, which may be configured to communicate with each othersuch as via a bus 902. In some embodiments, the processors 910 (e.g., aCentral Processing Unit (CPU), a Reduced Instruction Set Computing(RISC) processor, a Complex Instruction Set Computing (CISC) processor,a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), anASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, orany suitable combination thereof) may include, for example, a processor912 and a processor 914 that may execute the instructions 916. The term“processor” is intended to include multi-core processors that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.9 shows multiple processors 910, the machine 900 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory 930 may include a main memory 932, a static memory 934, and astorage unit 936, both accessible to the processors 910 such as via thebus 902. The main memory 930, the static memory 934, and storage unit936 store the instructions 916 embodying any one or more of themethodologies or functions described herein. The instructions 916 mayalso reside, completely or partially, within the main memory 932, withinthe static memory 934, within the storage unit 936, within at least oneof the processors 910 (e.g., within the processor's cache memory), orany suitable combination thereof, during execution thereof by themachine 900.

The I/O components 950 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 950 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components 950may include many other components that are not shown in FIG. 9. The I/Ocomponents 950 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various embodiments, the I/O components 950 may includeoutput components 952 and input components 954. The output components952 may include visual components (e.g., a display such as a plasmadisplay panel (PDP), a light emitting diode (LED) display, a liquidcrystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 954 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further embodiments, the I/O components 950 may include biometriccomponents 956, motion components 958, environmental components 960, orposition components 962, among a wide array of other components. Forexample, the biometric components 956 may include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 958 may include acceleration sensor componentsaccelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environmental components960 may include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometers that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensors (e.g., gas detection sensors to detectionconcentrations of hazardous gases for safety or to measure pollutants inthe atmosphere or other components that may provide indications,measurements, or signals corresponding to a surrounding physicalenvironment. The position components 962 may include location sensorcomponents (e.g., a GPS receiver component), altitude sensor components(e.g., altimeters or barometers that detect air pressure from whichaltitude may be derived), orientation sensor components (e.g.,magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 950 may include communication components 964 operableto couple the machine 900 to a network 980 or devices 970 via a coupling982 and a coupling 972, respectively. For example, the communicationcomponents 964 may include a network interface component or anothersuitable device to interface with the network 980. In further examples,the communication components 964 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 970 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 964 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 964 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components964, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (i.e., 930, 932, 934, and/or memory of theprocessor(s) 910) and/or storage unit 936 may store one or more sets ofinstructions and data structures (e.g., software) embodying or utilizedby any one or more of the methodologies or functions described herein.These instructions the instructions 916), when executed by processor(s)910, cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” “computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia and/or device-storage media include non-volatile memory, includingby way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), FPGA, and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms“machine-storage media,” “computer-storage media,” and “device-storagemedia” specifically exclude carrier waves, modulated data signals, andother such media, at least some of which are covered under the term“signal medium” discussed below.

Transmission Medium

In various embodiments, one or more portions of the network 980 may bean ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, aWAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portionof the PSTN, a plain old telephone service (POTS) network, a cellulartelephone network, a wireless network, a Wi-Fi® network, another type ofnetwork, or a combination of two or more such networks. For example, thenetwork 980 or a portion of the network 980 may include a wireless orcellular network, and the coupling 982 may be a Code Division MultipleAccess (CDMA) connection, a Global System for Mobile communications(GSM) connection, or another type of cellular or wireless coupling. Inthis example, the coupling 982 may implement any of a variety of typesof data transfer technology, such as Single Carrier Radio TransmissionTechnology (1×RTT), Evolution-Data Optimized (EVDO) technology, GeneralPacket Radio Service (GPRS) technology, Enhanced Data rates for GSMEvolution (EDGE) technology, third Generation Partnership Project (3GPP)including 3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long range protocols, or other data transfertechnology.

The instructions 916 may be transmitted or received over the network 980using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components964) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions916 may be transmitted or received using a transmission medium via thecoupling 972 (e.g., a peer-to-peer coupling) to the devices 970. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 916 for execution by the machine 900, and includes digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a matter as to encode information in the signal.

Computer-Readable Medium

The terms “machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

For some embodiments, the operations or features described herein areimplemented via a non-transitory computer-readable medium or as asystem.

The disclosed technology may be described in the context ofcomputer-executable instructions, such as software or program modules,being executed by a computer or processor. The computer-executableinstructions may comprise portions of computer program code, routines,programs, objects, software components, data structures, or other typesof computer-related structures that may be used to perform processesusing a computer. In some cases, hardware or combinations of hardwareand software may be substituted for software or used in place ofsoftware.

Computer program code used for implementing various operations oraspects of the disclosed technology may be developed using one or moreprogramming languages, including an object-oriented programming languagesuch as Java or C++, a procedural programming language such as the “C”programming language or Visual Basic, or a dynamic programming languagesuch as Python or JavaScript. In some cases, computer program code ormachine-level instructions derived from the computer program code mayexecute entirely on an end user's computer, partly on an end user'scomputer, partly on an end user's computer and partly on a remotecomputer, or entirely on a remote computer or server.

For purposes of this document, it should be noted that the dimensions ofthe various features depicted in the Figures may not necessarily bedrawn to scale.

For purposes of this document, reference in the specification to “anembodiment,” “one embodiment,” “some embodiments,” or “anotherembodiment” may be used to describe different embodiments and do notnecessarily refer to the same embodiment.

For purposes of this document, a connection may be a direct connectionor an indirect connection (e.g., via another part). In some cases, whenan element is referred to as being connected or coupled to anotherelement, the element may be directly connected to the other element orindirectly connected to the other element via intervening elements. Whenan element is referred to as being directly connected to anotherelement, then there are no intervening elements between the element andthe other element.

For purposes of this document, the term “based on” may be read as “basedat least in part on.”

For purposes of this document, without additional context, use ofnumerical terms such as a “first” object, a “second” object, and a“third” object may not imply an ordering of objects, but may instead beused for identification purposes to identify different objects.

For purposes of this document, the term “set” of objects may refer to a“set” of one or more of the objects.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A method comprising: determining, by one or morehardware processors, whether a plurality of existing data snapshotscomprises a minimal number of data snapshots for a target set of files;and after determining that the plurality of existing data snapshotscomprises the minimal number of data snapshots: periodically classifyingthe target set of files, by the one or more hardware processors, byperiodically: determining a rank of each file in the target set of filesto generate a set of file ranks for the target set of files; andassociating each file in the target set of files to a given filesetclass from a plurality of fileset classes based on a respective filerank from the set of file ranks and based on a given range of ranksassociated with the given fileset class, each fileset class of theplurality of fileset classes being associated with a different timeinterval in a plurality of snapshot generation time intervals and eachfileset class of the plurality of fileset classes being associated witha different range of ranks; and periodically generating, by the one ormore hardware processors, incremental data snapshots for the target setof files based on the plurality of fileset classes and according to theplurality of snapshot generation time intervals as associated with thetarget set of files, the periodically generating of the incremental datasnapshots comprising storing delta data for each file in the target setof files that has experienced a data block change or a file metadatachange.
 2. The method of claim 1, further comprising: generating, by theone or more hardware processors, the plurality of existing datasnapshots of the target set of files stored on a file system over timeaccording to a defined snapshot generation time interval.
 3. The methodof claim 1, wherein the determining of the rank of each file in thetarget set of files to generate the set of file ranks comprises: rankingeach file in the target set of files based on a set of ranking factors,the set of ranking factors comprising a frequency of file data change.4. The method of claim 1, wherein the periodically generating theincremental data snapshots for the target set of files based on theplurality of fileset classes and according to the plurality of snapshotgeneration time intervals as associated with the target set of filescomprises: for each individual fileset class of the plurality of filesetclasses, causing periodic generation of an incremental data snapshot fora given subset of files of the target set of files according to a givensnapshot generation time interval from the plurality of snapshotgeneration time intervals, the given subset of files and the givensnapshot generation time interval being associated with the individualfileset class.
 5. The method of claim 1, wherein the periodicallygenerating the incremental data snapshots for the target set of filesbased on the plurality of fileset classes and according to the pluralityof snapshot generation time intervals as associated with the target setof files comprises: adjusting, based on the plurality of fileset classesand according to the plurality of snapshot generation time intervals asassociated with the target set of files, policy data that determinesperiodic generation of incremental data snapshots for the target set offiles.
 6. The method of claim 1, wherein the plurality of filesetclasses comprises a first fileset class, a second fileset class, and athird fileset class, wherein the plurality of snapshot generation timeintervals comprises a first snapshot generation time interval associatedwith the first fileset class, a second snapshot generation time intervalassociated with the second fileset class, and a third snapshotgeneration time interval associated with the third fileset class,wherein the first snapshot generation time interval is greater than thesecond snapshot generation time interval, and wherein the secondsnapshot generation time interval is greater than the third snapshotgeneration time interval.
 7. The method of claim 6, further comprising:generating, by the one or more hardware processors, a plurality of datasnapshots of the target set of files stored on a file system over timeaccording to the second snapshot generation time interval.
 8. The methodof claim 1, wherein the periodically classifying each file in the targetset of files is performed after generation of a new data snapshot. 9.The method of claim 8, wherein the new data snapshot comprises a newincremental data snapshot.
 10. The method of claim 1, wherein each filein the target set of files is initially associated with a same filesetclass from the plurality of fileset classes.
 11. A system comprising: amemory storing instructions; and one or more hardware processorscommunicatively coupled to the memory and configured by the instructionsto perform operations comprising: determining whether a plurality ofexisting data snapshots comprises a minimal number of data snapshots fora target set of files; and after determining that the plurality ofexisting data snapshots comprises the minimal number of data snapshots:periodically classifying the target set of files by periodically:determining a rank of each file in the target set of files to generate aset of file ranks for the target set of files; and associating each filein the target set of files to a given fileset class from a plurality offileset classes based on a respective file rank from the set of fileranks and based on a given range of ranks associated with the givenfileset class, each fileset class of the plurality of fileset classesbeing associated with a different time interval in a plurality ofsnapshot generation time intervals and each fileset class of theplurality of fileset classes being associated with a different range ofranks; and periodically generating incremental data snapshots for thetarget set of files based on the plurality of fileset classes andaccording to the plurality of snapshot generation time intervals asassociated with the target set of files, the periodically generating ofthe incremental data snapshots comprising storing delta data for eachfile in the target set of files that has experienced a data block changeor a file metadata change.
 12. The system of claim 11, wherein theoperations further comprise: generating the plurality of existing datasnapshots of the target set of files stored on a file system over timeaccording to a defined snapshot generation time interval.
 13. The systemof claim 11, wherein the determining of the rank of each file in thetarget set of files to generate the set of file ranks comprises: rankingeach file in the target set of files based on a set of ranking factors,the set of ranking factors comprising a frequency of file data change.14. The system of claim 11, wherein the periodically generating theincremental data snapshots for the target set of files based on theplurality of fileset classes and according to the plurality of snapshotgeneration time intervals as associated with the target set of filescomprises: for each individual fileset class of the plurality of filesetclasses, causing periodic generation of an incremental data snapshot fora given subset of files of the target set of files according to a givensnapshot generation time interval from the plurality of snapshotgeneration time intervals, the given subset of files and the givensnapshot generation time interval being associated with the individualfileset class.
 15. The system of claim 11, wherein the periodicallygenerating the incremental data snapshots for the target set of filesbased on the plurality of fileset classes and according to the pluralityof snapshot generation time intervals as associated with the target setof files comprises: adjusting, based on the plurality of fileset classesand according to the plurality of snapshot generation time intervals asassociated with the target set of files, policy data that determinesperiodic generation of incremental data snapshots for the target set offiles.
 16. The system of claim 11, wherein the plurality of filesetclasses comprises a first fileset class, a second fileset class, and athird fileset class, wherein the plurality of snapshot generation timeintervals comprises a first snapshot generation time interval associatedwith the first fileset class, a second snapshot generation time intervalassociated with the second fileset class, and a third snapshotgeneration time interval associated with the third fileset class,wherein the first snapshot generation time interval is greater than thesecond snapshot generation time interval, and wherein the secondsnapshot generation time interval is greater than the third snapshotgeneration time interval.
 17. The system of claim 11, wherein theperiodically classifying each file in the target set of files isperformed after generation of a new data snapshot.
 18. The system ofclaim 11, wherein each file in the target set of files is initiallyassociated with a same fileset class from the plurality of filesetclasses.
 19. A non-transitory computer-readable storage mediumcomprising instructions that, when executed by a processing device,cause the processing device to perform operations comprising:determining whether a plurality of existing data snapshots comprises aminimal number of data snapshots for a target set of files; and afterdetermining that the plurality of existing data snapshots comprises theminimal number of data snapshots: periodically classifying the targetset of files by periodically: determining a rank of each file in thetarget set of files to generate a set of file ranks for the target setof files; and associating each file in the target set of files to agiven fileset class from a plurality of fileset classes based on arespective file rank from the set of file ranks and based on a givenrange of ranks associated with the given fileset class, each filesetclass of the plurality of fileset classes being associated with adifferent time interval in a plurality of snapshot generation timeintervals and each fileset class of the plurality of fileset classesbeing associated with a different range of ranks; and periodicallygenerating incremental data snapshots for the target set of files basedon the plurality of fileset classes and according to the plurality ofsnapshot generation time intervals as associated with the target set offiles, the periodically generating of the incremental data snapshotscomprising storing delta data for each file in the target set of filesthat has experienced a data block change or a file metadata change. 20.The non-transitory computer-readable storage medium of claim 19, whereinthe determining of the rank of each file in the target set of files togenerate the set of file ranks comprises: ranking each file in thetarget set of files based on a set of ranking factors, the set ofranking factors comprising a frequency of file data change.